In order to address the issue of increasing data rate requirements demanded for wireless communication systems, different schemes are being developed to allow multiple user terminals to communicate with a single base station by sharing the same channel (same time and frequency resources) while achieving high data throughputs. Spatial Division Multiple Access (SDMA) represents one such approach that has recently emerged as a popular technique for the next generation of communication systems.
In SDMA systems, a base station (i.e., an access point) may transmit or receive different signals to or from a plurality of mobile user terminals (i.e., stations) at the same time, using the same frequency band. In order to achieve reliable data communication, user terminals may need to be located in sufficiently different directions. Independent signals may simultaneously be transmitted from each of multiple space-separated antennas at the base station. Consequently, the combined transmissions may be directional, i.e., the signal that is dedicated for each user terminal may be relatively strong in the direction of that particular user terminal and sufficiently weak in directions of other user terminals. Similarly, the base station may simultaneously receive on the same frequency the combined signals from multiple user terminals through each of multiple antennas separated in space, and the combined received signals from the multiple antennas may be split into independent signals transmitted from each user terminal by applying the appropriate signal processing technique.
A multiple-input multiple-output (MIMO) wireless system employs a number (NT) of transmit antennas and a number (NR) of receive antennas for data transmission. A MIMO channel formed by the NT transmit and NR receive antennas may be decomposed into NS spatial streams, where, for all practical purposes, NS=min{NT, NR}. The NS spatial streams may be used to transmit NS independent data streams to achieve greater overall throughput.
In a multiple-access MIMO system based on SDMA, an access point can communicate with one or more user terminals at any given moment. If the access point communicates with a single user terminal, then the NT transmit antennas are associated with one transmitting entity (either the access point or the user terminal), and the NR receive antennas are associated with one receiving entity (either the user terminal or the access point). The access point can also communicate with multiple user terminals simultaneously via SDMA. For SDMA, the access point utilizes multiple antennas for data transmission and reception, and each of the user terminals typically utilizes less than the number of access point antennas for data transmission and reception. When SDMA is transmitted from an access point, NS=min{NT, sum(NR)}, where sum(NT) represents the summation of all user terminal receive antennas. When SDMA is transmitted to an access point, NS=min{sum(NT), NR}, where sum(NT) represents the summation of all user terminal transmit antennas.
An access point may support a plurality of stations in the same frequency band and at the same time. The access point may perform beamforming to spatially direct signals meant for different STAs. In downlink, the access point calculates the transmit power and directions of the beamformers for each spatial stream. A minimum mean square error (MMSE) beamforming algorithm may be used to calculate the beamforming direction and power of a stream while nulling out the interference across stations for which the stream is not meant and getting the best signal strength at the station to which the stream is targeted.
There may be several optimization criteria within the MMSE beamforming framework, one of which is the transmit power minimization. In the transmit power minimization algorithm, the total transmit power at an access point is minimized while calculating the beamformers and power values that achieve a given set of target SNR values. However, the current state of the art does not include any of the practical system constraints, such as frequency selective channels, power amplifier distortion, imperfect channel state information as well as limited computing power at the access point in MMSE beamforming algorithm.
In addition, the MMSE algorithm performs expensive matrix computations in each iteration. The complexity of MMSE algorithm may be reduced by selecting a good initial estimate of the downlink power allocation based on target SINR values. If the initial target SINR values are accurate, reduced number of iterations of the MMSE beamforming algorithm may provide suitable results.
Therefore, there is a need in the art for a technique to calculate accurate SINR targets for the MMSE beamforming algorithm considering practical constraints to decrease the complexity of the MMSE algorithm.